TY - GEN
T1 - Introducing CALMED
T2 - 17th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023
AU - Sousa, Annanda
AU - Young, Karen
AU - d’Aquin, Mathieu
AU - Zarrouk, Manel
AU - Holloway, Jennifer
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Automatic Emotion Detection (ED) aims to build systems to identify users’ emotions automatically. This field has the potential to enhance HCI, creating an individualised experience for the user. However, ED systems tend to perform poorly on people with Autism Spectrum Disorder (ASD). Hence, the need to create ED systems tailored to how people with autism express emotions. Previous works have created ED systems tailored for children with ASD but did not share the resulting dataset. Sharing annotated datasets is essential to enable the development of more advanced computer models for ED within the research community. In this paper, we describe our experience establishing a process to create a multimodal annotated dataset featuring children with a level 1 diagnosis of autism. In addition, we introduce CALMED (Children, Autism, Multimodal, Emotion, Detection), the resulting multimodal emotion detection dataset featuring children with autism aged 8–12. CALMED includes audio and video features extracted from recording files of study sessions with participants, together with annotations provided by their parents into four target classes. The generated dataset includes a total of 57,012 examples, with each example representing a time window of 200 ms (0.2 s). Our experience and methods described here, together with the dataset shared, aim to contribute to future research applications of affective computing in ASD, which has the potential to create systems to improve the lives of people with ASD.
AB - Automatic Emotion Detection (ED) aims to build systems to identify users’ emotions automatically. This field has the potential to enhance HCI, creating an individualised experience for the user. However, ED systems tend to perform poorly on people with Autism Spectrum Disorder (ASD). Hence, the need to create ED systems tailored to how people with autism express emotions. Previous works have created ED systems tailored for children with ASD but did not share the resulting dataset. Sharing annotated datasets is essential to enable the development of more advanced computer models for ED within the research community. In this paper, we describe our experience establishing a process to create a multimodal annotated dataset featuring children with a level 1 diagnosis of autism. In addition, we introduce CALMED (Children, Autism, Multimodal, Emotion, Detection), the resulting multimodal emotion detection dataset featuring children with autism aged 8–12. CALMED includes audio and video features extracted from recording files of study sessions with participants, together with annotations provided by their parents into four target classes. The generated dataset includes a total of 57,012 examples, with each example representing a time window of 200 ms (0.2 s). Our experience and methods described here, together with the dataset shared, aim to contribute to future research applications of affective computing in ASD, which has the potential to create systems to improve the lives of people with ASD.
KW - Affective Computing
KW - Autism
KW - Multimodal Dataset
KW - Multimodal Emotion Detection
UR - https://www.scopus.com/pages/publications/85169064930
U2 - 10.1007/978-3-031-35681-0_43
DO - 10.1007/978-3-031-35681-0_43
M3 - Conference Publication
AN - SCOPUS:85169064930
SN - 9783031356803
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 657
EP - 677
BT - Universal Access in Human-Computer Interaction - 17th International Conference, UAHCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings
A2 - Antona, Margherita
A2 - Stephanidis, Constantine
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 July 2023 through 28 July 2023
ER -